Random Dot Product Graph Models for Social Networks
By Stephen J. Young, E. Scheinerman. Workshop on Algorithms and Models for the Web-Graph, 2007
320 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/43832246af239e1e234a1d5ab6f8ca22fdb0daba
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Jure Leskovec
- Christos Faloutsos
- Deepayan Chakrabarti
- Jon M. Kleinberg
- Zoubin Ghahramani
- Charu C. Aggarwal
- Jaewon Yang
- Mark Newman
In Statistical and Nonlinear Physics
- Jure Leskovec
- Christos Faloutsos
- Jon M. Kleinberg
- Charu C. Aggarwal
- Deepayan Chakrabarti
- Manuel Gomez-Rodriguez
- Zoubin Ghahramani
- Bernhard Schölkopf
Citations in the corpus
- A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs (2011, background): “One example of a latent space model is the random dot product graph (RDPG) model (Young and Scheinerman 2007).”
- A Limit Theorem for Scaled Eigenvectors of Random Dot Product Graphs (2013, background): “For example, in random dot product graphs [30], the link function is the dot product: namely, the edge probabilities are the dot products of the latent positions.”
- Statistical Inference on Random Dot Product Graphs: a Survey (2017, background): “The random dot product graph (RDPG) of Young and Scheinerman [106] is an especially tractable latent position graph; here, the latent space is an appropriately constrained subspace of Euclidean space R, and the link function is simply the dot or inner product of the pair of d-dimensional latent posi”
- Kronecker Graphs: An Approach to Modeling Networks (2008, methodology): “We note that the view of Kronecker graphs where every node is dscribed with a set of features and the initiator matrix encodes the probability of linkinggiven the attribute values of two nodes somewhat resembles the Random dot product graph model (Young and Scheinerman, 2007; Nickel, 2008).”
- Asymptotically perfect seeded graph matching without edge correlation (and applications to inference) (2025, unknown): “Our analysis will be couched within the context of Random Dot Product Graphs (RDPG) of [30], a class of latent position graph models [31] that are particularly amenable to subsequent analysis [29].”
- Comparing Foundation Models using Data Kernels (2023, background): “The Random Dot Product Graph (RDPG) [27] is a flexible model for undirected, hollow, symmetric random graphs that has been applied successfully in a variety of domains [12, 7, 25].”